Economically disadvantaged youth residing in mountain tourist communities represent an important and understudied rural population. These communities typically include a large percentage of children that are English language learners. Our NSF STEM Career Connections project, A Model for Preparing Economically-Disadvantaged Rural Youth for the Future STEM Workplace, investigates strategies that help middle school youth in these communities to envision a broader range of workforce opportunities, especially in STEM and computing careers. This poster highlights the initial findings of an innovative model that involves working with local schools and community partners to support the integration of local career contexts, engineering phenomena, 3D printing technologies, career connections, and mentorship into formal educational experiences to motivate and prepare rural youth for future STEM careers.
We focus on select classrooms at two middle schools and describe the implementation of a novel 3D printing curriculum during the 2020-2021 school-year. Two STEM teachers implemented the five-week curriculum with approximately 300 students per quarter. To create a rich inquiry-driven learning environment, the curriculum uses an instructional design approach called storylining. This approach is intended to promote coherence, relevance, and meaning from the students’ perspectives by using students’ questions to drive investigations and lessons. Students worked towards answering the question: “How can we support animals with physical disabilities so they can perform daily activities independently?” Students engaged in the engineering design process by defining, developing, and optimizing solutions to develop and print prosthetic limbs for animals with disabilities using 3D modeling, a unique augmented reality application, and 3D printing. In order to embed connections to STEM careers and career pathways, some students received mentorship and guidance from local STEM professionals who work in related fields.
This poster will describe the curriculum and its implementation across two quarters at two middle schools in the US rural mountain west, as well as the impact on students’ interest in STEM and computing careers. During the first quarter students engaged in the 3D printing curriculum, but did not have access to the STEM career and career pathway connections mentorship piece. During the second quarter, the project established a partnership with a local STEM business -- a medical research institute that utilizes 3D printing and scanning for creating human surgical devices and procedures -- to provide mentorship to the students. Volunteers from this institute served as ongoing mentors for the students in each classroom during the second quarter. The STEM mentors guided students through the process of designing, testing, and optimizing their 3D models and 3D printed prosthetics, providing insights into how students’ learning directly applies to the medical industry.
Different forms of student data such as cognitive interviews and pre/post STEM interest and spatial thinking surveys were collected and analyzed to understand the benefits of the career connections mentorship component. Preliminary findings suggest the relationship between local STEM businesses and students is important to motivate youth from rural areas to see themselves being successful in STEM careers and helping them to realize the benefits of engaging with emerging engineering technologies.
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Impact of Vaccination and Testing in an Urban Campus Model for the SARS-CoV-2 Pandemic
The crisis induced by the Coronavirus pandemic severely impacted educational institutes. Even with vaccination efforts underway in 2021, it was not clear that sufficient confidence will be achieved for schools to reopen soon. This paper considers the impact of testing rates in addition to vaccination rates in order to reduce infections and hospitalizations and evaluates strategies that allow educational institute in urban settings to remain open. These strategies are also applicable to big campus style businesses and would help planning to keep the businesses open and help the economy.
Our analysis is based on a graph model where nodes represent population groups and edges represent population exchanges due to commuting populations. The commuting population is associated with edges and is associated with one of the end nodes of the edge during part of the time period and with the other node during the remainder of the time period.
The progression of the disease at each node is determined via compartment models, that include vaccination rates and testing to place infected people in quarantine along with consideration of asymptomatic and symptomatic populations. Applying this to a university population in Chicago with a substantial commuter population, chosen to be 80% of the school’s population as an illustration, provides an analysis which specifies benefits of testing and vaccination strategies over a time period of 150 days.
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- Award ID(s):
- 2028274
- NSF-PAR ID:
- 10399095
- Date Published:
- Journal Name:
- ACM Conference on Information Technology for Social Good
- Page Range / eLocation ID:
- 183 to 190
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Chronograms—phylogenies with branch lengths proportional to time—represent key data on timing of evolutionary events, allowing us to study natural processes in many areas of biological research. Chronograms also provide valuable information that can be used for education, science communication, and conservation policy decisions. Yet, achieving a high-quality reconstruction of a chronogram is a difficult and resource-consuming task. Here we present DateLife, a phylogenetic software implemented as an R package and an R Shiny web application available at www.datelife.org, that provides services for efficient and easy discovery, summary, reuse, and reanalysis of node age data mined from a curated database of expert, peer-reviewed, and openly available chronograms. The main DateLife workflow starts with one or more scientific taxon names provided by a user. Names are processed and standardized to a unified taxonomy, allowing DateLife to run a name match across its local chronogram database that is curated from Open Tree of Life’s phylogenetic repository, and extract all chronograms that contain at least two queried taxon names, along with their metadata. Finally, node ages from matching chronograms are mapped using the congruification algorithm to corresponding nodes on a tree topology, either extracted from Open Tree of Life’s synthetic phylogeny or one provided by the user. Congruified node ages are used as secondary calibrations to date the chosen topology, with or without initial branch lengths, using different phylogenetic dating methods such as BLADJ, treePL, PATHd8, and MrBayes. We performed a cross-validation test to compare node ages resulting from a DateLife analysis (i.e, phylogenetic dating using secondary calibrations) to those from the original chronograms (i.e, obtained with primary calibrations), and found that DateLife’s node age estimates are consistent with the age estimates from the original chronograms, with the largest variation in ages occurring around topologically deeper nodes. Because the results from any software for scientific analysis can only be as good as the data used as input, we highlight the importance of considering the results of a DateLife analysis in the context of the input chronograms. DateLife can help to increase awareness of the existing disparities among alternative hypotheses of dates for the same diversification events, and to support exploration of the effect of alternative chronogram hypotheses on downstream analyses, providing a framework for a more informed interpretation of evolutionary results.
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The ongoing COVID-19 pandemic has created major public health and socio-economic challenges across the United States. Among them are challenges to the educational system where college administrators are struggling with the questions of how to mitigate the risk and spread of diseases on their college campus. To help address this challenge, we developed a flexible computational framework to model the spread and control of COVID-19 on a residential college campus. The modeling framework accounts for heterogeneity in social interactions, activities, environmental and behavioral risk factors, disease progression, and control interventions. The contribution of mitigation strategies to disease transmission was explored without and with interventions such as vaccination, quarantine of symptomatic cases, and testing. We show that even with high vaccination coverage (90%) college campuses may still experience sizable outbreaks. The size of the outbreaks varies with the underlying environmental and socio-behavioral risk factors. Complementing vaccination with quarantine and mass testing was shown to be paramount for preventing or mitigating outbreaks. Though our quantitative results are likely provisional on our model assumptions, sensitivity analysis confirms the robustness of their qualitative nature.
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Low, Nicola (Ed.)Background While booster vaccinations clearly reduce the risk of severe Coronavirus Disease 2019 (COVID-19) and death, the impact of boosters on Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infections has not been fully characterized: Doing so requires understanding their impact on asymptomatic and mildly symptomatic infections that often go unreported but nevertheless play an important role in spreading SARS-CoV-2. We sought to estimate the impact of COVID-19 booster doses on SARS-CoV-2 infections in a vaccinated population of young adults during an Omicron BA.1-predominant period. Methods and findings We implemented a cohort study of young adults in a college environment (Cornell University’s Ithaca campus) from a period when Omicron BA.1 was the predominant SARS-CoV-2 variant on campus (December 5 to December 31, 2021). Participants included 15,800 university students who completed initial vaccination series with vaccines approved by the World Health Organization for emergency use, were enrolled in mandatory at-least-weekly surveillance polymerase chain reaction (PCR) testing, and had no positive SARS-CoV-2 PCR test within 90 days before the start of the study period. Robust multivariable Poisson regression with the main outcome of a positive SARS-CoV-2 PCR test was performed to compare those who completed their initial vaccination series and a booster dose to those without a booster dose. A total of 1,926 unique SARS-CoV-2 infections were identified in the study population. Controlling for sex, student group membership, date of completion of initial vaccination series, initial vaccine type, and temporal effect during the study period, our analysis estimates that receiving a booster dose further reduces the rate of having a PCR-detected SARS-CoV-2 infection relative to an initial vaccination series by 56% (95% confidence interval [42%, 67%], P < 0.001). While most individuals had recent booster administration before or during the study period (a limitation of our study), this result is robust to the assumed delay over which a booster dose becomes effective (varied from 1 day to 14 days). The mandatory active surveillance approach used in this study, under which 86% of the person-days in the study occurred, reduces the likelihood of outcome misclassification. Key limitations of our methodology are that we did not have an a priori protocol or statistical analysis plan because the analysis was initially done for institutional research purposes, and some analysis choices were made after observing the data. Conclusions We observed that boosters are effective, relative to completion of initial vaccination series, in further reducing the rate of SARS-CoV-2 infections in a college student population during a period when Omicron BA.1 was predominant; booster vaccinations for this age group may play an important role in reducing incidence of COVID-19.more » « less
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Abstract Motivation The de Bruijn graph is fundamental to the analysis of next generation sequencing data and so, as datasets of DNA reads grow rapidly, it becomes more important to represent de Bruijn graphs compactly while still supporting fast assembly. Previous implementations of compact de Bruijn graphs have not supported node or edge deletion, however, which is important for pruning spurious elements from the graph.
Results Belazzougui et al. (2016b) recently proposed a compact and fully dynamic representation, which supports exact membership queries and insertions and deletions of both nodes and edges. In this paper, we give a practical implementation of their data structure, supporting exact membership queries and fully dynamic edge operations, as well as limited support for dynamic node operations. We demonstrate experimentally that its performance is comparable to that of state-of-the-art implementations based on Bloom filters.
Availability and implementation Our source-code is publicly available at https://github.com/csirac/dynamicDBG under an open-source license.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3524458.3547269
